The purpose of blackthe holdout evaluation [1]} is to test a model on blacka different data blackfrom what it was trained on. In this method, the dataset is randomly divided into blacka training set and a test set, the latter being the holdout data. The effectiveness of a model (\(\hat{R}\) ) for the predicted item ranking \(\hat{Z}\)  can be calculated as follows on the holdout data [2]}:
\(\hat{R}_{\mathrm {holdout}}(\hat{Z})=\frac{1}{|\mathcal {U}|} \sum _{u \in \mathcal {U}} \frac{1}{\left|\mathcal {S}_{u}^{*}\right|} \sum _{i \in \mathcal {S}_{u}^{*}} c(\hat{Z}_{u, i})\) 
